Abstract
For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between genes are commonly used. In case of a family-based design, it can be problematic when there is a large between-family variation in expression levels. We propose here a gene coexpression network analysis for family studies. We build a coexpression network for each family and then combine the results. We applied our approach to data provided for analysis in the Genetic Analysis Workshop 19 and compared it to 2 naïve approaches—ignoring correlations among the expressions and decorrelating the gene expression by using the residuals of a mixed model—and a single-probe analysis. Our approach seemed to better deal with heterogeneity with regard to the naïve approaches. The naïve approaches did not provide any significant results, while our approach detected genes via indirect effects. It also detected more genes than the single-probe analysis.
Highlights
Weighted gene coexpression network is a widely used method for studying biological networks based on pairwise correlations
For family data Kraft et al [3] noted that testing association between expression levels and traits without taking into account the family structure can lead to spurious results, especially when the number of families is small and in the presence of large between-family variation
We propose a novel strategy for network analyses in a small set of relatively large families
Summary
Weighted gene coexpression network is a widely used method for studying biological networks based on pairwise correlations This method provides more insight in the underlying biological mechanisms and offers a tool for dimension reduction by summarizing identified modules (clusters) of genes [1, 2]. How to perform such an analysis for family data is an open question. We propose a novel strategy for network analyses in a small set of relatively large families For this family-based approach, we first construct family-specific coexpression networks and test for
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
